---
title: "Asthma in NYC Neighborhoods"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    source_code: embed
    vertical_layout: fill
    navbar:
      - icon: fa-home
        href: https://prisrinivasan.github.io/p8105_final/
        align: left
    theme: journal
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(viridis)
library(leaflet)
library(plotly)
library(sf)
library(sp)
library(devtools)
library(rgdal)
library(magick)
tidy_asthma = 
  read_csv(file = "./final_data/tidy_asthma.csv")
```

```{r data import, message=FALSE, warning=FALSE, echo=FALSE, include=FALSE}
#tidied dataset
all_data = 
read.csv(file = "./final_data/all_available_data.csv") %>% 
  janitor::clean_names() %>% 
  filter(name %in% c("Homes with Cockroaches", 
                     "Poverty", 
                     "Public School Children (5-14 Yrs Old) with Asthma", 
                     "Public School Children (5-14 Yrs Old) with Persistent Asthma", 
                     "Asthma Hospitalizations (Children 5 to 17 Yrs Old)"),
        geo_type_name %in% c("UHF42", "Borough"), 
        measure %in% c("Percent", "Rate")
         ) %>% 
  mutate(geo_join_id = as.character(geo_join_id),
         geo_join_id = if_else(geo_place_name == "Bensonhurst - Bay Ridge", "209", geo_join_id),
         geo_join_id = as.factor(geo_join_id)) %>% 
  pivot_wider(
    names_from = name, 
    values_from = data_value
    ) %>%
  mutate(time_period = dplyr::recode(time_period, "2010-2011" = "2011", "2011-2012" = "2012", "2012-2013" = "2013", "2013-2014" = "2014", "2010-14" = "2011")) %>%
  filter(time_period %in% c("2011", "2012", "2013", "2014")) %>% 
  janitor::clean_names() %>% 
  dplyr::select(geo_type_name, geo_join_id, time_period, geo_place_name, measure, indicator_id, homes_with_cockroaches, poverty, public_school_children_5_14_yrs_old_with_asthma, public_school_children_5_14_yrs_old_with_persistent_asthma, asthma_hospitalizations_children_5_to_17_yrs_old)
df1 = all_data  %>% 
  dplyr::select(geo_type_name, geo_join_id, time_period, geo_place_name, measure, indicator_id, homes_with_cockroaches) %>% 
  drop_na()
df2 = all_data %>% 
  dplyr::select(geo_type_name, geo_join_id, time_period, geo_place_name, measure, indicator_id, poverty) %>% 
  drop_na()
df3 = all_data %>% 
  dplyr::select(geo_type_name, geo_join_id, time_period, geo_place_name, measure, indicator_id, public_school_children_5_14_yrs_old_with_asthma) %>% 
  drop_na()
df4 = all_data %>% 
  dplyr::select(geo_type_name, geo_join_id, time_period, geo_place_name, measure, indicator_id, public_school_children_5_14_yrs_old_with_persistent_asthma) %>% 
  drop_na()
df5 = all_data %>% 
  dplyr::select(geo_type_name, geo_join_id, time_period, geo_place_name, measure, indicator_id, asthma_hospitalizations_children_5_to_17_yrs_old) %>% 
  drop_na()
tidy_asthma = 
  full_join(df1, df2, by = c("geo_type_name", "geo_join_id", "time_period", "geo_place_name")) %>% 
  full_join(df3, by = c("geo_type_name", "geo_join_id", "time_period", "geo_place_name")) %>% 
  full_join(df4, by = c("geo_type_name", "geo_join_id", "time_period", "geo_place_name")) %>% 
  full_join(df5, by = c("geo_type_name", "geo_join_id", "time_period", "geo_place_name")) %>% 
  dplyr::select(geo_type_name, geo_join_id, time_period, geo_place_name, homes_with_cockroaches, poverty, public_school_children_5_14_yrs_old_with_asthma, public_school_children_5_14_yrs_old_with_persistent_asthma, asthma_hospitalizations_children_5_to_17_yrs_old) %>% 
  mutate(time_period = as.numeric(levels(time_period)) [time_period],
         geo_join_id = as.character(as.numeric(levels(geo_join_id)) [geo_join_id]),
         geo_place_name = as.character(geo_place_name)) 
```


Column {data-width=650}
-----------------------------------------------------------------------

### Chart A: Asthma in NYC Neighborhoods

```{r}
#labeling options for axes
xa_1 = list(
  title = "Neighborhood",
  showticklabels = FALSE
)
ya = list(
  title = "Asthma Prevalence"
)
tidy_asthma %>%
  filter(geo_type_name == "UHF42") %>%
   arrange(geo_join_id) %>%
    plot_ly(
    x = ~geo_join_id, 
    y = ~public_school_children_5_14_yrs_old_with_asthma, 
    color = ~geo_place_name,
    text = ~geo_place_name,
    frame = ~time_period,
    hoverinfo = "text",
    type = 'bar'
  ) %>% 
  layout(xaxis = xa_1, yaxis = ya, showlegend = FALSE)
```

### Description 

The _Asthma in NYC Neighborhoods_ chart displays asthma rates in different neighborhoods from 2011-2014. Users can move between different years or click `Play` to watch neighborhood asthma rates change across NYC.

This chart shows the distribution of asthma across neighborhoods over time. The Bronx consistently has the highest asthma prevalence with Hunt's Point as one of the most impacted neighborhoods. Manhattan is generally the borough with the lowest asthma prevalence, although certain neighborhoods such as East Harlem are consistently high. 


Column {data-height = 650}
--------------------------------------------------------------------

### Chart B: Asthma in NYC Boroughs

```{r}
#creating new x axis options and reusing y axis label options
xa_2 = list(
  title = "Borough"
)
tidy_asthma %>%
  filter(geo_type_name == "Borough") %>%
  arrange(geo_join_id) %>%
  plot_ly(
    x = ~geo_place_name, 
    y = ~public_school_children_5_14_yrs_old_with_asthma, 
    color = ~geo_place_name,
    text = ~geo_place_name,
    frame = ~time_period,
    hovertemplate = paste(
      "%{x}
",
      "Rate: %{y:.2f}"),
    type = 'bar'
  ) %>% 
  layout(xaxis = xa_2, yaxis = ya, showlegend = FALSE)
```

### Description

The _Asthma in NYC Boroughs_ chart shows the regional asthma trends from 2011-2014. Moving through different years shows the changes in regional asthma prevalence over time. Press `Play` to visualize these changes.

The Bronx is consistently the highest rate of asthma with Queens following. Brooklyn asthma rates have increased by nearly 15% from 2011 to 2014. Overall, all of the boroughs demonstrate increased asthma prevalence from 2011 to 2014.